加拿大阿尔伯塔大学和美国俄克拉荷马州立大学联合博士后—定量/统计遗传学

加拿大阿尔伯塔大学和美国俄克拉荷马州立大学联合博士后—定量/统计遗传学

阿尔伯塔大学（University of Alberta），简称“UA”，成立于1908年，是坐落于加拿大阿尔伯塔省会埃德蒙顿的一所世界著名研究型大学，是加拿大U15研究型大学联盟创始成员、世界大学联盟以及世界能源大学联盟成员。

阿尔伯塔大学是加拿大最大的研究型大学之一，在地球科学，石油化工，化学，商学，农学，生物医学等学科最为著名。校友包含第16任加拿大总理，三位诺贝尔奖得主（包括2020年诺贝尔生理学医学奖得主霍顿），75位罗德学者（总数居世界名牌大学前列），141位加拿大皇家学会成员，111位加拿大首席研究教授。

阿尔伯塔大学的人工智能专业在全球居于领先地位，全球顶级计算机科学机构排名CSRankings [4]2010-2020年度人工智能领域世界排名第37名，其中人工智能和机器学习世界第6名。强化学习之父Rich Sutton、以及Alpha Go的主要作者大卫·席尔瓦 （David Silver）和黄士杰（Aja Huang）均来自阿尔伯塔大学。

Quantitative Geneticist Postdoctoral Research Associate

Employer

University of Alberta and Oklahoma State University

Location

University of Alberta, Edmonton, Alberta CA and Oklahoma State University, Stillwater, Oklahoma USA

Salary

competitive scale at NSF in the USA and NSERC in Canada

Closing date

Aug 1, 2024

Position: Quantitative Geneticist Postdoctoral Research Associate Position for BFF-AFIRMS project

BFF-AFIRMS, (Best Future Forest: advanced forest genomics and integrative resource management system), is a transformative initiative aimed at digitalizing forest resource management and tree improvement in Alberta, Canada. This is a joint effort across Government of Alberta, industry partners of Tree Improvement of Alberta, University of Alberta (CA), and Oklahoma State University (USA), aiming to integrate high-throughput genotyping, predictive analytics, and decision-making support to ensure responsible stewardship of forest genetic resources in the face of climate challenges, as well as increasing demand for sustainable forest ecosystems and products.

BFF-AFIRMS invites applications for full-time Quantitative/Statistical Geneticist postdoc/research associate positions to lead quantitative genetics and statistical modeling efforts within the project. The successful candidates will play a key role in leveraging existing genomic, phenotypic, and environmental data to dissect components of variance, estimate effective population size, identify genetic and environmental associations, and conduct genomic prediction and optimize selection and breeding designs.

Qualifications:

1. Ph.D. degree in Statistics, Quantitative Genetics, Plant Breeding, or related field with a focus on associative analysis and genomic prediction.

2. Strong expertise in statistical modeling, quantitative genetics, genetic mapping, genomic selection, and association analysis.

3. Proficiency in programming languages such as Python and R for large-scale data analysis and modeling.

4. Experience with genotyping technologies, and population genomics in plant or tree species is highly desirable.

5. Familiarity with tree species or plant breeding programs is desirable, but not required.

6. Excellent analytical and communication skills, team collaboration abilities, and a passion for translating genomic knowledge into practical solutions for end-users.

Benefit Highlights:

1. Competitive compensation- full time employment with competitive compensation scale at federal agencies such as NSF in the USA and NSERC in Canada.

2. Dynamic work environment- be part of a highly dynamic multiple-disciplinary team environment across academia, government, and industry partnerships.

3. Flexible work arrangement- benefit from flexibility with hybrid work (on-site and work from home) arrangements after the initial employment process.

How to Apply and Required Documents

A cover letter addressing research interest, experience and skills that fulfill the requirements. A full C.V. Most recent or most significant publications. Contact information for 3 potential referees.

Candidates should prepare the documents as ONE PDF FILE and submit to bioinformaticsosu@gmail.com

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